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Fix rendering LT

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This commit is contained in:
FilippoOlivo
2025-03-14 11:41:23 +01:00
committed by Nicola Demo
parent bc62ef9120
commit c92193d49c
1 changed files with 62 additions and 41 deletions
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103
pina/label_tensor.py
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@@ -33,10 +33,10 @@ class LabelTensor(torch.Tensor):
@property
def tensor(self):
"""
Give the tensor part of the :class:`~pina.label_tensor.LabelTensor`
Returns the tensor part of the :class:`~pina.label_tensor.LabelTensor`
object.
:return: tensor part of the :class:`~pina.label_tensor.LabelTensor`.
:return: Tensor part of the :class:`~pina.label_tensor.LabelTensor`.
:rtype: torch.Tensor
"""
@@ -44,10 +44,15 @@ class LabelTensor(torch.Tensor):
def __init__(self, x, labels):
"""
Construct a :class:`~pina.label_tensor.LabelTensor` by passing a dict of
the labels and a :class:`torch.Tensor`. Internally, the initialization
method will store check the compatibility of the labels with the tensor
shape.
Initialize the :class:`~pina.label_tensor.LabelTensor` instance, by
checking the consistency of the labels and the tensor. Specifically, the
labels must match the following conditions:
- At each dimension, the number of labels must match the size of the \
dimension.
- At each dimension, the labels must be unique.
The labels can be passed in the following formats:
:Example:
>>> from pina import LabelTensor
@@ -57,11 +62,18 @@ class LabelTensor(torch.Tensor):
>>> tensor = LabelTensor(
>>> torch.rand((2000, 3)),
... ["a", "b", "c"])
The keys of the dictionary are the dimension indices, and the values are
dictionaries containing the labels and the name of the dimension. If
the labels are passed as a list, these are assigned to the last
dimension.
:param torch.Tensor x: The tensor to be casted as a
:class:`~pina.label_tensor.LabelTensor`.
:param labels: Labels to assign to the tensor.
:type labels: str | list[str] | dict
:raises ValueError: If the labels are not consistent with the tensor.
"""
# Avoid unused argument warning. x is not used in the constructor
# of the parent class.
# pylint: disable=unused-argument
super().__init__()
if labels is not None:
self.labels = labels
@@ -71,7 +83,7 @@ class LabelTensor(torch.Tensor):
@property
def full_labels(self):
"""
Gives the full labels of the tensor, even for the dimensions that are
Returns the full labels of the tensor, even for the dimensions that are
not labeled.
:return: The full labels of the tensor
@@ -89,7 +101,7 @@ class LabelTensor(torch.Tensor):
@property
def stored_labels(self):
"""
Gives the labels stored inside the instance.
Returns the labels stored inside the instance.
:return: The labels stored inside the instance.
:rtype: dict
@@ -99,7 +111,7 @@ class LabelTensor(torch.Tensor):
@property
def labels(self):
"""
Give the labels of the last dimension of the instance.
Returns the labels of the last dimension of the instance.
:return: labels of last dimension
:rtype: list
@@ -111,8 +123,9 @@ class LabelTensor(torch.Tensor):
@labels.setter
def labels(self, labels):
"""
Set the parameter ``_labels`` by checking the type of the input labels
and handling it accordingly. The following types are accepted:
Set labels stored insider the instance by checking the type of the
input labels and handling it accordingly. The following types are
accepted:
- **list**: The list of labels is assigned to the last dimension.
- **dict**: The dictionary of labels is assigned to the tensor.
@@ -134,7 +147,7 @@ class LabelTensor(torch.Tensor):
else:
raise ValueError("labels must be list, dict or string.")
def _init_labels_from_dict(self, labels: dict):
def _init_labels_from_dict(self, labels):
"""
Store the internal label representation according to the values
passed as input.
@@ -146,7 +159,7 @@ class LabelTensor(torch.Tensor):
tensor_shape = self.shape
def validate_dof(dof_list, dim_size: int):
def validate_dof(dof_list, dim_size):
"""Validate the 'dof' list for uniqueness and size."""
if len(dof_list) != len(set(dof_list)):
raise ValueError("dof must be unique")
@@ -187,7 +200,7 @@ class LabelTensor(torch.Tensor):
def _init_labels_from_list(self, labels):
"""
Given a ``list`` of dof, this method update the internal label
Given a list of dof, this method update the internal label
representation by assigning the dof to the last dimension.
:param labels: The label(s) to update.
@@ -203,17 +216,25 @@ class LabelTensor(torch.Tensor):
def extract(self, labels_to_extract):
"""
Extract the subset of the original tensor by returning all the positions
corresponding to the passed ``label_to_extract``.
corresponding to the passed ``label_to_extract``. If ``label_to_extract``
is a dictionary, the keys are the dimension names and the values are the
labels to extract. If a single label or a list of labels is passed, the
last dimension is considered.
:param labels_to_extract: The label(s) to extract. If a single label or
a list of labels is passed, the last dimension is considered.
If a dictionary is passed, the keys are the dimension names and the
values are the labels to extract.
:Example:
>>> from pina import LabelTensor
>>> labels = {1: {'dof': ["a", "b", "c"], 'name': 'space'}}
>>> tensor = LabelTensor(torch.rand((2000, 3)), labels)
>>> tensor.extract("a")
>>> tensor.extract(["a", "b"])
>>> tensor.extract({"space": ["a", "b"]})
:param labels_to_extract: The label(s) to extract.
:type labels_to_extract: str | list[str] | tuple[str] | dict
:return: The extracted tensor with the updated labels.
:rtype: LabelTensor
:raises TypeError: Labels are not ``str``, ``list of str`` or ``dict``
:raises TypeError: Labels are not ``str``, ``list[str]`` or ``dict``
properly setted.
:raises ValueError: Label to extract is not in the labels ``list``.
"""
@@ -298,13 +319,13 @@ class LabelTensor(torch.Tensor):
:param list[LabelTensor] tensors:
:class:`~pina.label_tensor.LabelTensor` instances to concatenate
:param int dim: dimensions on which you want to perform the operation
:param int dim: Dimensions on which you want to perform the operation
(default is 0)
:return: A new :class:`LabelTensor' instance obtained by concatenating
the input instances, with the updated labels.
:return: A new :class:`LabelTensor` instance obtained by concatenating
the input instances.
:rtype: LabelTensor
:raises ValueError: either number dof or dimensions names differ
:raises ValueError: either number dof or dimensions names differ.
"""
if not tensors:
@@ -353,7 +374,7 @@ class LabelTensor(torch.Tensor):
:param list[LabelTensor] tensors: A list of tensors to stack.
All tensors must have the same shape.
:return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained
by stacking the input tensors, with the updated labels.
by stacking the input tensors.
:rtype: LabelTensor
"""
@@ -389,7 +410,7 @@ class LabelTensor(torch.Tensor):
Give the ``dtype`` of the tensor. For more details, see
:meth:`torch.dtype`.
:return: dtype of the tensor
:return: The data type of the tensor.
:rtype: torch.dtype
"""
@@ -427,19 +448,19 @@ class LabelTensor(torch.Tensor):
def append(self, tensor, mode="std"):
"""
Appends a given tensor to the current tensor along the last dimension.
This method supports two types of appending operations:
1. **Standard append** ("std"): Concatenates the input tensor with the
1. **Standard append** ("std"): Concatenates the input tensor with the \
current tensor along the last dimension.
2. **Cross append** ("cross"): Creates a cross-product of the current
tensor and the input tensor by repeating them in a cross-product
fashion, then concatenates the result along the last dimension.
2. **Cross append** ("cross"): Creates a cross-product of the current \
tensor and the input tensor.
:param tensor: The tensor to append to the current tensor.
:type tensor: LabelTensor
:param mode: The append mode to use. Defaults to "std".
:param mode: The append mode to use. Defaults to ``st``.
:type mode: str, optional
:return: A new `LabelTensor` obtained by appending the input tensor.
:return: A new :class:`LabelTensor` instance obtained by appending the
input tensor.
:rtype: LabelTensor
:raises ValueError: If the mode is not "std" or "cross".
@@ -468,7 +489,7 @@ class LabelTensor(torch.Tensor):
raise ValueError('mode must be either "std" or "cross"')
@staticmethod
def vstack(label_tensors):
def vstack(tensors):
"""
Stack tensors vertically. For more details, see :meth:`torch.vstack`.
@@ -480,7 +501,7 @@ class LabelTensor(torch.Tensor):
:rtype: LabelTensor
"""
return LabelTensor.cat(label_tensors, dim=0)
return LabelTensor.cat(tensors, dim=0)
# This method is used to update labels
def _update_single_label(
@@ -592,11 +613,11 @@ class LabelTensor(torch.Tensor):
def sort_labels(self, dim=None):
"""
Sort the labels along the specified dimension and apply the same sorting
to the :class:`torch.Tensor` part of the instance.
Sort the labels along the specified dimension and apply. It applies the
same sorting to the tensor part of the instance.
:param int dim: The dimension along which to sort the labels.
If ``None``, the last dimension (``ndim - 1``) is used.
If ``None``, the last dimension is used.
:return: A new tensor with sorted labels along the specified dimension.
:rtype: LabelTensor
"""